Helper Module for Deep Learning.

Module containing VAE utilities.

Code: https://github.com/YannDubs/disentangling-vae

pynet.models.vae.utils.add_labels(input_image, labels)[source]¶

Adds labels next to rows of an image.

Parameters

input_image: PIL.Image

the image to which to add the labels.

labels: list

the list of labels to plot.

pynet.models.vae.utils.get_traversal_range(mean=0, std=1, max_traversal=0.475)[source]¶

Return the corresponding traversal range in absolute terms.

Parameters

mean: float, default 0

normal distribution mean.

std: float, default 1

normal distribution sigma.

max_traversal: float, default 0.475

the maximum displacement induced by a latent traversal. Symmetrical traversals are assumed. If m >= 0.5 then uses absolute value traversal, if m < 0.5 uses a percentage of the distribution (quantile), e.g. for the prior the distribution is a standard normal so m = 0.45 corresponds to an absolute value of 1.645 because 2m = 90%% of a standard normal is between -1.645 and 1.645. Note in the case of the posterior, the distribution is not standard normal anymore.

Returns

out: 2-uplet

traversal range.

pynet.models.vae.utils.make_mosaic_img(arr)[source]¶

Converts a grid of image array into a single mosaic.

Parameters

arr: numpy.ndarray (ROWS, COLS, C, H, W)

organized images all of the same size to generate the mosaic.

pynet.models.vae.utils.reconstruct_traverse(model, data, n_per_latent=8, n_latents=None, is_posterior=False, filename=None)[source]¶

Creates a figure whith first row for original images, second are reconstructions, rest are traversals (prior or posterior) of the latent dimensions.

Parameters

model: nn.Module

the trained network.

data: torch.Tensor (N, C, H, W)

data to be reconstructed.

n_per_latent: int, default 8

the number of points to include in the traversal of a latent dimension, i.e. the number of columns.

n_latents: int, default None

the number of latent dimensions to display, i.e. the number of rows. If ‘None’ uses all latents.

is_posterior: bool, default False

whether to sample from the posterior.

filename: str, default None

path to save the finale image.

pynet.models.vae.utils.traversals(model, device, data=None, n_per_latent=8, n_latents=None)[source]¶

Plot traverse through all latent dimensions (prior or posterior) one by one and plots a grid of images where each row corresponds to a latent traversal of one latent dimension.

Parameters

model: nn.Module

the trained network.

device: torch.device

the device.

data: torch.Tensor (N, C, H, W), default None

data to use for computing the posterior. If ‘None’ then use the mean of the prior (all zeros) for all other dimensions.

n_per_latent: int, default 8

the number of points to include in the traversal of a latent dimension, i.e. the number of columns.

n_latents: int, default None

the number of latent dimensions to display, i.e. the number of rows. If ‘None’ uses all latents.

pynet.models.vae.utils.traverse_line(model, idx, n_samples, data=None)[source]¶

Return latent samples corresponding to a traversal of a latent variable indicated by idx.

Parameters

model: nn.Module

the trained network.

idx: int

index of continuous dimension to traverse. If the continuous latent vector is 10 dimensional and idx = 7, then the 7th dimension will be traversed while all others are fixed.

n_samples: int

number of samples to generate.

data: torch.Tensor (N, C, H, W), default None

data to use for computing the posterior. If ‘None’ then use the mean of the prior (all zeros) for all other dimensions.

Returns

samples: torch.Tensor (n_samples, latent_size)